TY - CHAP
T1 - Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases
AU - van Veen, Rick
AU - Talavera Martinez, Lidia
AU - Kogan, Rosalie Vered
AU - Meles, Sanne
AU - Mudali, Deborah
AU - Roerdink, J. B. T. M.
AU - Massa, Federico
AU - Grazzini, Matteo
AU - Obeso, J.A.
AU - Rodriguez-Oroz, M.C.
AU - Leenders, Klaus
AU - Renken, Remco
AU - de Vries, J.J.G.
AU - Biehl, Michael
PY - 2018
Y1 - 2018
N2 - Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data.
AB - Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data.
U2 - 10.3233/978-1-61499-929-4-280
DO - 10.3233/978-1-61499-929-4-280
M3 - Chapter
SN - 978-1-61499-928-7
VL - 310
T3 - Frontiers in Artificial Intelligence and Applications
SP - 280
EP - 289
BT - Applications of Intelligent Systems
A2 - Petkov, N.
A2 - Strisciuglio, N.
A2 - Travieso-González, C.
PB - IOS Press
ER -